Contextual content for voice user interfaces

ABSTRACT

The present disclosure describes techniques for dynamically determining when information is to be output to a user, as well as what information is to be output to a user. A natural language processing system may receive, from a first device, first data representing information to be output at a first point during a skill session. The natural language processing system may also receive, from a second device, second data representing a natural language input. The natural language processing system may determine a skill component is to execute with respect to the natural language input. The natural language processing system may send, to the skill component, second data representing the natural language input. The natural language processing system may receive, from the skill component, an indication that an ongoing first skill session with the second device has reached the first point. After receiving the indication and based at least in part on system usage data associated with at least one user, the natural language processing system may determine third data representing a prompt corresponding to the information and send, to the second device, the third data for output.

CROSS-REFERENCE TO RELATED APPLICATION DATA

This application is a continuation of, and claims the benefit ofpriority of, U.S. Non-Provisional patent application Ser. No.16/455,530, filed Jun. 27, 2019, and entitled “CONTEXTUAL CONTENT FORVOICE USER INTERFACES,” scheduled to issue as U.S. Pat. No. 11,227,592,the contents of which is expressly incorporated herein by reference inits entirety.

BACKGROUND

Speech recognition systems have progressed to the point where humans caninteract with computing devices using their voices. Such systems employtechniques to identify the words spoken by a human user based on thevarious qualities of a received audio input. Speech recognition combinedwith natural language understanding processing techniques enablespeech-based user control of a computing device to perform tasks basedon the user's spoken commands. Speech recognition and natural languageunderstanding processing techniques may be referred to collectively orseparately herein as speech processing. Speech processing may alsoinvolve converting a user's speech into text data which may then beprovided to various text-based software applications.

Speech processing may be used by computers, hand-held devices, telephonecomputer systems, kiosks, and a wide variety of other devices to improvehuman-computer interactions.

BRIEF DESCRIPTION OF DRAWINGS

For a more complete understanding of the present disclosure, referenceis now made to the following description taken in conjunction with theaccompanying drawings.

FIG. 1 is a conceptual diagram illustrating the system configured tooutput information to a user during execution of a skill session, inaccordance with embodiments of the present disclosure.

FIG. 2 is a conceptual diagram of components of the system, inaccordance with embodiments of the present disclosure.

FIG. 3 illustrates data stored in an information output storage, inaccordance with embodiments of the present disclosure.

FIG. 4 illustrates data stored in a timing storage, in accordance withembodiments of the present disclosure.

FIG. 5 is a conceptual diagram illustrating how an information selectioncomponent may determine when information is to be output to a user, andwhat information to output to the user, in accordance with embodimentsof the present disclosure.

FIGS. 6A through 6C are a signal flow diagram illustrating howinformation may be output to a user, in accordance with embodiments ofthe present disclosure.

FIG. 7 illustrates a system for managing a goal-oriented dialog usingmultiple dialog models, in accordance with embodiments of the presentdisclosure.

FIGS. 8A and 8B are a process flow diagram illustrating how aninformation selection component of a natural language processing systemmay execute, in accordance with embodiments of the present disclosure.

FIG. 9 is a block diagram conceptually illustrating example componentsof a device, in accordance with embodiments of the present disclosure.

FIG. 10 is a block diagram conceptually illustrating example componentsof a system, in accordance with embodiments of the present disclosure.

FIG. 11 illustrates an example of a computer network for use with theoverall system, in accordance with embodiments of the presentdisclosure.

DETAILED DESCRIPTION

Automatic speech recognition (ASR) is a field of computer science,artificial intelligence, and linguistics concerned with transformingaudio data associated with speech into text representative of thatspeech. Similarly, natural language understanding (NLU) is a field ofcomputer science, artificial intelligence, and linguistics concernedwith enabling computers to derive meaning from text input containingnatural language. ASR and NLU are often used together as part of aspeech processing system. Text-to-speech (TTS) is a field of computerscience concerning transforming textual and/or other data into audiodata that is synthesized to resemble human speech.

A system may cause skills to perform actions in response to naturallanguage inputs (e.g., text/typed inputs and/or spoken inputs). Forexample, for the natural language input “play Adele music,” a musicskill may be invoked to output music sung by an artist named Adele. Forfurther example, for the natural language input “turn on the lights,” asmart home skill may be invoked to turn on “smart” lights associatedwith a user's profile. In another example, for the natural languageinput “book me a ride to my favorite restaurant,” a ride sharingservice's skill may be invoked to book a trip to the user's favoriterestaurant (e.g., as represented in the user's profile), and the ridesharing service's skill may cause the system to output synthesizedspeech representing such booking. Actions, in the foregoing examples,correspond to the outputting of music, turning on of “smart” lights, andbooking of the trip coupled with output of the synthesized speech. Assuch, as used herein, an “action” may refer to some result of a system'sprocessing.

As used herein, a skill may refer to software, hardware, and/or firmwarerunning on a system that enables the system to execute specificfunctionality in order to provide data or produce some other output inresponse to a natural language input. In at least some examples, a skillmay be server-based. In at least some other examples, a skill may beakin to a software application running on a traditional computingdevice. Example skills may include weather information skills, musicplaying skills, or the like. The functionality described herein as askill may be referred to using many different terms, such as an action,bot, app, or the like.

A skill may be created and altered by a skill developer. As used herein,a “skill developer” may refer to a user of a system that has specificpermissions to generate and alter data to create and alter runtimefunctionality of a skill.

In at least some examples, a skill developer may provide a system withdata representing information to be output to a user as part of theuser's interaction with a skill. The information to be output may not bea response to a natural language input. For example, a skill developermay provide a system with data representing a user of a smart home skillis to receive information representing how the user may obtain adiscount on a new smart home device. In another example, a skilldeveloper may provide a system with data representing a user of avehicle skill is to receive information representing how the user mayobtain a discount on the user's next oil change. One skilled in the artwill appreciate that the foregoing examples are not exhaustive, and thatother examples are possible.

In at least some examples, a skill developer may, in addition toproviding data representing information to be output, provide datarepresenting when information is to be output to users of a skill. Suchdata may, in at least some examples, be rather rigid in that the datamay represent the information is to be output at specific times. Forexample, a skill developer may indicate information is to be output eachtime a user has indicated the user has finished its present interactionwith a skill. For further example, a skill developer may indicateinformation is to be output once during a free trial period of aservice. In another example, a skill developer may indicate informationis to be output when a user causes a skill to be invoked an nth time.

The present disclosure provides techniques for dynamically determiningwhen information is to be output to a user, as well as what informationis to be output to a user. The teachings of the present disclosure maylimit when information is output to instances when the information ismost likely to be relevant to the user. For example, a system maydetermine information representing a trial subscription to a service isnot relevant to a user that is already a subscribing member of theservice. For further example, this results in an improved userexperience.

A system may be configured to incorporate user permissions and may onlyperform activities disclosed herein if approved by a user. As such, thesystems, devices, components, and techniques described herein would betypically configured to restrict processing where appropriate and onlyprocess user information in a manner that ensures compliance with allappropriate laws, regulations, standards, and the like. The system andtechniques can be implemented on a geographic basis to ensure compliancewith laws in various jurisdictions and entities in which the componentsof the system and/or user are located.

A skill developer may provide an input (e.g., typed input), to a skilldeveloper device, representing information to be output to users of askill. The skill developer device may generate first data representingthe information to be output to users and including a skill identifiercorresponding to the skill. The skill developer device may send thefirst data to the natural language processing system 120.

The skill developer may also provide an input (e.g., typed input), tothe skill developer device, representing a point, in a skill session,when information is to be output to users of the skill. As used herein,a “point” in a skill session may refer to after the natural languageprocessing system 120 receives a natural language input to be processedby the skill but prior to the natural language processing system 120invoking the skill to execute, may refer to after a user has indicatedthe user no longer desired to interact with the skill (e.g., after theuser provides a natural language input representing the user wants toend the skill session), may refer to a duration of a skill session(e.g., after the skill session has been occurring for a minute), mayrefer to a number of natural language inputs of a skill session (e.g.,after the user has provided 3 natural language inputs as part of theskill session), or may refer to some other developer definable andnatural language processing system understandable point in a skillsession. In at least some examples, a duration of a skill session may betracked by a natural language processing system and/or a skill systemexecuting with respect to the skill session. A natural languageprocessing system may measure a duration of a skill session startingfrom when the natural language processing system receiving a firstnatural language input of the skill session. A skill system may measurea duration of a skill session starting from when the skill system isfirst called by a natural language processing system to execute withrespect to the skill session.

As used herein, “skill session” may refer to data transmissions (such asrelating to multiple natural language inputs and skill outputs) betweena skill and a device(s) that all relate to a single originating naturallanguage input. Thus, the data transmissions of a skill session may beassociated with a same skill session identifier. The skill sessionidentifier may be used by components of the overall system 100 to trackinformation across the skill session. For example, a device may send thenatural language processing system 120 data corresponding to “Alexa,play jeopardy.” The natural language processing system 120 may invoke agaming skill to play the game jeopardy. As part of playing jeopardy, thegaming skill may send, to the device via the natural language processingsystem 120, data corresponding to a jeopardy statement to be output to auser(s). A user may then respond to the statement, which the devicesends as data to the natural language processing system 120, which sendsthe data to the gaming skill. The sending of data from the device to thenatural language processing system 120 (and ultimately the gaming skill)and the sending of data from gaming skill to the natural languageprocessing system 120 (and ultimately to the device) may all correspondto a single skill session identifier. In some examples, a skillsession-initiating natural language input may start with a wakeword andend with a command, such as “Alexa, play jeopardy,” where “Alexa” is thewakeword and “play jeopardy” is the command. Subsequent natural languageinputs of the same skill session may start with speaking of a wakeword.Each natural language input of a skill session may be associated with aunique natural language input identifier such that multiple naturallanguage input identifiers may be associated with a single skill sessionidentifier.

The skill developer device may generate second data representing thepoint in the skill session when information is to be output to users ofthe skill, and including the skill identifier corresponding to theskill. The skill developer device may send the second data to thenatural language processing system 120.

The natural language processing system 120 may associate theinformation, to be output to users of the skill, with the skill'sidentifier in storage. The natural language processing system 120 mayalso associate the point, when information is to be output, with theskill's identifier in storage.

FIG. 1 shows the system 100 configured to output information to a userduring execution of a skill session. Although the figures and discussionof the present disclosure illustrate certain steps in a particularorder, the steps described may be performed in a different order (aswell as certain steps removed or added) without departing from thepresent disclosure. As shown in FIG. 1, the system 100 may include oneor more devices (110 a/110 b), local to a user 5, in communication witha natural language processing system 120 across one or more networks199.

After the natural language processing system 120 associates theinformation and point with the skill identifier in storage(s) (asdescribed above), the device 110 a may receive audio corresponding to aspoken natural language input (corresponding to the skill) originatingfrom the user 5. The device 110 a may generate audio data correspondingto the audio and may send the audio data to the natural languageprocessing system 120. Alternatively, the device 110 b may receive atext/typed natural language input (corresponding to the skill) from theuser 5. The device 110 b may generate text data corresponding to thetext/typed input and may send the text data to the natural languageprocessing system 120. Alternatively, the device 110 b may detect atouch event corresponding to a portion of the device 110 b's displaypresenting a natural language input (e.g., in the form of a virtualbutton, selectable text, etc.). The device 110 b may generate text datacorresponding to the natural language input and may send the text datato the natural language processing system 120.

The device 110 may send the audio data and/or the text data to thenatural language processing system 120 via a companion applicationinstalled on the device 110. A companion application may enable thedevice 110 to communicate with the natural language processing system120 via the network(s) 199. An example companion application is theAmazon Alexa application that may be installed on a smart phone, tablet,or the like.

The natural language processing system 120 may receive (150) datarepresenting the natural language input. The third data may be audiodata when the natural language input is a spoken natural language input.The third data may be text data when the natural language input is atext/typed natural language input.

The natural language processing system 120 may cause (152) the skill toexecute a first skill session with respect to the natural languageinput. The may include the natural language processing system 120sending, to the skill, data representing a determined intent of the userand one or more named entities represented in the natural languageinput. Such data may be generated as a result of NLU processing or SLUprocessing as described herein.

At some point during the skill session, the natural language processingsystem 120 may determine (154) a current point of the skill sessioncorresponds to the point represented in the data received from the skilldeveloper device. After the natural language processing system 120determines the current point corresponds to the stored point, thenatural language processing system 120 may determine (156), based oncontext data, that information is to be output to the user. Anon-limiting list of context data that may be considered includes a timeof day, a history representing information previously output to theuser, data representing when other users of the natural languageprocessing system 120 have requested further information regardingoutput information, etc.

After the natural language processing system 120 determines informationis to be output to the user 5, the natural language processing system120 may determine (158) the information, received from the skilldeveloper device, is to be output to the user 5 based at least in parton the information being associated with the skill identifier instorage. The natural language processing system 120 may thereafteroutput (160) the information to the user 5. After outputting theinformation to the user 5, the natural language processing system 120may cause (162) the skill to resume execution of the skill session.

The system 100 may operate using various components as described in FIG.2. The various components may be located on a same or different physicaldevices. Communication between various components may occur directly oracross a network(s) 199.

An audio capture component(s), such as a microphone or array ofmicrophones of the device 110 a, captures audio 11. The device 110 aprocesses audio data, representing the audio 11, to determine whetherspeech is detected. The device 110 a may use various techniques todetermine whether audio data includes speech. In some examples, thedevice 110 a may apply voice activity detection (VAD) techniques. Suchtechniques may determine whether speech is present in audio data basedon various quantitative aspects of the audio data, such as the spectralslope between one or more frames of the audio data; the energy levels ofthe audio data in one or more spectral bands; the signal-to-noise ratiosof the audio data in one or more spectral bands; or other quantitativeaspects. In other examples, the device 110 a may implement a limitedclassifier configured to distinguish speech from background noise. Theclassifier may be implemented by techniques such as linear classifiers,support vector machines, and decision trees. In still other examples,the device 110 a may apply Hidden Markov Model (HMM) or Gaussian MixtureModel (GMM) techniques to compare the audio data to one or more acousticmodels in storage, which acoustic models may include modelscorresponding to speech, noise (e.g., environmental noise or backgroundnoise), or silence. Still other techniques may be used to determinewhether speech is present in audio data.

Once speech is detected in audio data representing the audio 11, thedevice 110 a may use a wakeword detection component 220 to performwakeword detection to determine when a user intends to speak an input tothe natural language processing system 120. An example wakeword is“Alexa.”

Wakeword detection is typically performed without performing linguisticanalysis, textual analysis, or semantic analysis. Instead, the audiodata, representing the audio 11, is analyzed to determine if specificcharacteristics of the audio data match preconfigured acousticwaveforms, audio signatures, or other data to determine if the audiodata “matches” stored audio data corresponding to a wakeword.

Thus, the wakeword detection component 220 may compare audio data tostored models or data to detect a wakeword. One approach for wakeworddetection applies general large vocabulary continuous speech recognition(LVCSR) systems to decode audio signals, with wakeword searching beingconducted in the resulting lattices or confusion networks. LVCSRdecoding may require relatively high computational resources. Anotherapproach for wakeword detection builds HMIs for each wakeword andnon-wakeword speech signals, respectively. The non-wakeword speechincludes other spoken words, background noise, etc. There can be one ormore HMMs built to model the non-wakeword speech characteristics, whichare named filler models. Viterbi decoding is used to search the bestpath in the decoding graph, and the decoding output is further processedto make the decision on wakeword presence. This approach can be extendedto include discriminative information by incorporating a hybrid DNN-HMMdecoding framework. In another example, the wakeword detection component220 may be built on deep neural network (DNN)/recursive neural network(RNN) structures directly, without HMI being involved. Such anarchitecture may estimate the posteriors of wakewords with contextinformation, either by stacking frames within a context window for DNN,or using RNN. Follow-on posterior threshold tuning or smoothing isapplied for decision making. Other techniques for wakeword detection,such as those known in the art, may also be used.

Once the wakeword is detected, the device 110 a may “wake” and begintransmitting audio data 211, representing the audio 11, to the naturallanguage processing system 120. The audio data 211 may include datacorresponding to the wakeword, or the device 110 a may remove theportion of the audio corresponding to the wakeword prior to sending theaudio data XAA11 to the natural language processing system 120.

An orchestrator component 230 may receive the audio data 211. Theorchestrator component 230 may include memory and logic that enables theorchestrator component 230 to transmit various pieces and forms of datato various components of the system 100, as well as perform otheroperations.

The orchestrator component 230 may send the audio data 211 to an ASRcomponent 250. The ASR component 250 transcribes the audio data 211 intoASR results data (e.g., text data). The text data output by the ASRcomponent 250 represents one or more than one (e.g., in the form of ann-best list) ASR hypotheses representing speech represented in the audiodata 211. The ASR component 250 interprets the speech in the audio data211 based on a similarity between the audio data 211 and pre-establishedlanguage models. For example, the ASR component 250 may compare theaudio data 211 with models for sounds (e.g., subword units, such asphonemes, etc.) and sequences of sounds to identify words that match thesequence of sounds of the speech represented in the audio data 211. TheASR component 250 outputs text data representing one or more ASRhypotheses. The text data output by the ASR component 250 may include atop scoring ASR hypothesis or may include an n-best list of ASRhypotheses. Each ASR hypothesis may be associated with a respectivescore. Each score may indicate a confidence of ASR processing performedto generate the ASR hypothesis with which the score is associated.

The device 110 b may receive a text/typed natural language input. Thedevice 110 b may generate text data 213 representing the text/typednatural language input. The device 110 b may send the text data 213 tothe natural language processing system 120. The orchestrator component230 may receive the text data 213.

The orchestrator component 230 may send text data (e.g., text dataoutput by the ASR component 250 or the received text data 213) to an NLUcomponent 260.

The NLU component 260 attempts to make a semantic interpretation of thephrase(s) or statement(s) represented in the received text data. Thatis, the NLU component 260 determines one or more meanings associatedwith the phrase(s) or statement(s) represented in the text data based onwords represented in the text data. The NLU component 260 determines anintent representing an action that a user desires be performed as wellas pieces of the text data that allow a device (e.g., the device 110,the natural language processing system 120, a skill, a skill system,etc.) to execute the intent. For example, if the text data correspondsto “play Adele music,” the NLU component 260 may determine an intentthat a skill output music and may identify “Adele” as an artist. Forfurther example, if the text data corresponds to “what is the weather,”the NLU component 260 may determine an intent that a skill outputweather information associated with a geographic location of the device110. In another example, if the text data corresponds to “turn off thelights,” the NLU component 260 may determine an intent that a skill turnoff lights associated with the device 110 and/or the user 5. The NLUcomponent 260 may output NLU results data (which may include tagged textdata, indicators of intent, etc.).

As described above, the natural language processing system 120 mayperform speech processing using two different components (e.g., the ASRcomponent 250 and the NLU component 260). One skilled in the art willappreciate that the natural language processing system 120, in at leastsome examples, may implement a spoken language understanding (SLU)component that is configured to process audio data 211 to generate NLUresults data.

In some examples, the SLU component may be equivalent to the ASRcomponent 250 and the NLU component 260. For example, the SLU componentmay process audio data 211 and generate NLU data. The NLU data mayinclude intent data and/or slot data so that directives may bedetermined based on the intent data and/or the slot data. While the SLUcomponent may be equivalent to a combination of the ASR component 250and the NLU component 260, the SLU component may process audio data 211and directly generate the NLU data, without an intermediate step ofgenerating text data (as does the ASR component 250). As such, the SLUcomponent may take audio data 211 representing speech and attempt tomake a semantic interpretation of the speech. That is, the SLU componentmay determine a meaning associated with the speech and then implementthat meaning. For example, the SLU component may interpret audio data211 representing speech from the user 5 in order to derive a desiredaction. In some examples, the SLU component outputs a most likely NLUresponse (e.g., hypothesis) recognized in the audio data 211, ormultiple hypotheses in the form of a lattice or an N-best list withindividual hypotheses corresponding to confidence scores or other scores(such as probability scores, etc.).

The natural language processing system 120 may include one or moreskills 290 configured to execute with respect to NLU results data (ordata representing NLU results data). For example, a weather skill mayenable the natural language processing system 120 to output weatherinformation, a car service skill may enable the natural languageprocessing system 120 to book a trip with respect to a taxi or ridesharing service, a restaurant skill may enable the natural languageprocessing system 120 to order a pizza with respect to the restaurant'sonline ordering system, etc. A skill 290 may operate in conjunctionbetween the natural language processing system 120 and other devices,such as the device 110, in order to complete certain functions. Inputsto a skill 290 may come from speech processing interactions or throughother interactions or input sources. A skill 290 may include hardware,software, firmware, or the like that may be dedicated to a particularskill 290 or shared among different skills 290.

In addition or alternatively to being implemented by the naturallanguage processing system 120, a skill 290 may be implemented by askill system 225. Such may enable a skill system 225 to execute specificfunctionality in order to provide data or perform some other actionrequested by a user.

A skill may be associated with a domain, such as a smart home domain, amusic domain, a video domain, a flash briefing domain, a shoppingdomain, and/or a custom domain.

The natural language processing system 120 may be configured with asingle skill 290 dedicated to interacting with more than one skillsystem 225.

The natural language processing system 120 may include a TTS component280. The TTS component 280 may generate audio data (e.g., synthesizedspeech) from text data using one or more different methods. Text datainput to the TTS component 280 may come from a skill 290, theorchestrator component 230, or another component of the natural languageprocessing system 120.

In one method of synthesis called unit selection, the TTS component 280matches text data against a database of recorded speech. The TTScomponent 280 selects matching units of recorded speech and concatenatesthe units together to form audio data. In another method of synthesiscalled parametric synthesis, the TTS component 280 varies parameterssuch as frequency, volume, and noise to generate audio data including anartificial speech waveform. Parametric synthesis uses a computerizedvoice generator, sometimes called a vocoder.

The natural language processing system 120 may include a userrecognition component 295. In at least some examples, the userrecognition component 295 may be implemented as a skill 290, or as partof a skill system 225.

The user recognition component 295 may recognize one or more users usinga variety of data. The user recognition component 295 may take as inputthe audio data 211 and/or the text data 213. The user recognitioncomponent 295 may perform user recognition by comparing speechcharacteristics, in the audio data 211, to stored speech characteristicsof users. The user recognition component 295 may additionally oralternatively perform user recognition by comparing biometric data(e.g., fingerprint data, iris data, etc.), received by the naturallanguage processing system 120 in correlation with a natural languageinput, to stored biometric data of users. The user recognition component295 may additionally or alternatively perform user recognition bycomparing image data (e.g., including a representation of at least afeature of a user), received by the natural language processing system120 in correlation with a natural language input, with stored image dataincluding representations of features of different users. The userrecognition component 295 may perform other or additional userrecognition processes, including those known in the art. For aparticular natural language input, the user recognition component 295may perform processing with respect to stored data of users associatedwith the device 110 that captured the natural language input.

The user recognition component 295 determines whether a natural languageinput originated from a particular user. For example, the userrecognition component 295 may generate a first value representing alikelihood that a natural language input originated from a first user, asecond value representing a likelihood that the natural language inputoriginated from a second user, etc. The user recognition component 295may also determine an overall confidence regarding the accuracy of userrecognition operations.

The user recognition component 295 may output a single user identifiercorresponding to the most likely user that originated the naturallanguage input. Alternatively, the user recognition component 295 mayoutput multiple user identifiers (e.g., in the form of an N-best list)with respective values representing likelihoods of respective usersoriginating the natural language input. The output of the userrecognition component 295 may be used to inform NLU processing,processing performed by a skill 290 or skill system 225, as well asprocessing performed by other components of the natural languageprocessing system 120 and/or other systems.

The natural language processing system 120 may include profile storage270. The profile storage 270 may include a variety of informationrelated to individual users, groups of users, devices, etc. thatinteract with the natural language processing system 120. As usedherein, a “profile” refers to a set of data associated with a user,group of users, device, etc. The data of a profile may includepreferences specific to the user, group of users, device, etc.; inputand output capabilities of one or more devices; internet connectivityinformation; user bibliographic information; subscription information;as well as other information.

The profile storage 270 may include one or more user profiles, with eachuser profile being associated with a different user identifier. Eachuser profile may include various user identifying information. Each userprofile may also include preferences of the user and/or one or moredevice identifiers, representing one or more devices registered to theuser. Each user profile may include identifiers of skills that the userhas enabled. When a user enables a skill, the user is providing thenatural language processing system 120 with permission to allow theskill to execute with respect to the user's inputs. If a user does notenable a skill, the natural language processing system 120 may notpermit the skill to execute with respect to the user's inputs.

The profile storage 270 may include one or more group profiles. Eachgroup profile may be associated with a different group profileidentifier. A group profile may be specific to a group of users. Thatis, a group profile may be associated with two or more individual userprofiles. For example, a group profile may be a household profile thatis associated with user profiles associated with multiple users of asingle household. A group profile may include preferences shared by allthe user profiles associated therewith. Each user profile associatedwith a group profile may additionally include preferences specific tothe user associated therewith. That is, each user profile may includepreferences unique from one or more other user profiles associated withthe same group profile. A user profile may be a stand-alone profile ormay be associated with a group profile. A group profile may include oneor more device profiles representing one or more devices associated withthe group profile.

The profile storage 270 may include one or more device profiles. Eachdevice profile may be associated with a different device identifier.Each device profile may include various device identifying information.Each device profile may also include one or more user identifiers,representing one or more user profiles associated with the deviceprofile. For example, a household device's profile may include the useridentifiers of users of the household.

The natural language processing system 120 may include an outputinformation storage 255. As described, a skill developer device maysend, to the natural language processing system 120, data representinginformation to be output to users of a skill, and that such data mayinclude the skill's identifier. The natural language processing system120 may associate the received information and corresponding skillidentifier in the output information storage 255 (as illustrated in FIG.3). A skill identifier may be associated with a single piece ofinformation (e.g., skill identifiers 1, 3, and 4 in FIG. 3) or more thanone piece of information (e.g., skill identifier 2 in FIG. 3).

The natural language processing system 120 may include a timing storage265. As described, a skill developer device may send, to the naturallanguage processing system 120, data representing a point, in a skillsession, when information is to be output to users of a skill, and thatsuch data may include the skill's identifier. The natural languageprocessing system 120 may associate the received point in the skillsession and corresponding skill identifier in the timing storage 265 (asillustrated in FIG. 4). A skill identifier may be associated with asingle point in a skill session (e.g., skill identifiers 1, 3, and 4 inFIG. 4) or more than one point in a skill session (e.g., skillidentifier 2 in FIG. 4).

As described above, a skill 290 may be invoked to execute with respectto an intent and one or more named entities determined from NLUprocessing or SLU processing. When a skill 290 is invoked, the naturallanguage processing system 120 may generate a skill session identifierand associate the skill session identifier with the data sent to theskill 290.

As also described above, a skill session may occur over a duration oftime, and may include various natural language inputs and correspondingskill outputs. At some point during the skill session, the skill 290executing with respect to the skill session identifier (or anothercomponent of the natural language processing system 120, such as theorchestrator component 230) may determine a present point in the skillsession corresponds to a skill session point represented in the timingstorage 265.

For example, the skill 290 may be configured to read payload data and/orcorresponding metadata to determine a present point of the skillsession. For example, the skill 290 may determine payload datarepresents a natural language input to end a skill session. Such mayinclude the skill 290 determining the payload data includes a <Cancel>NLU intent or other like NLU intent. For further example, the skill 290may read metadata to determine a present natural language input(represented in payload data corresponding to the currently being readmetadata) corresponds to a nth natural language input of the skillsession. In another example, the skill 290 may read metadata todetermine a length of time that the skill session has been ongoing. Oneskilled in the art will appreciate that the foregoing capabilities ofthe skill 290 are illustrative, and that the skill 290 may be configuredto read payload data and/or metadata for the purpose of determiningadditional or other points in skill sessions that may be represented inthe timing storage 265.

The skill 290 may determine whether the determined present point of theskill session is associated with the skill's identifier in the timingstorage 265. If the skill 290 determines the present point in the skillsession corresponds to a point in a skill session associated with theskill's identifier in the timing storage 265 (e.g., corresponding to apoint in a skill session a skill developer has indicated information isto be output to the user), the skill 290 may send, to an informationselection component 275 of the natural language processing system 120,an output information request 505 (as illustrated in FIG. 5). The outputinformation request 505 may include, for example, the skill'sidentifier, a domain to which the skill identifier is associated, theskill session identifier, a user identifier (representing the userproviding the natural language input(s) of the skill session), and/or adevice identifier (representing the device 110 that captured the naturallanguage input(s) of the skill session).

Users of the natural language processing system 120 may provide thenatural language processing system 120 with personal information. Auser's personal information may be used, with user permission, todetermine when information, provided by a skill developer, is to beoutput to the user. The natural language processing system 120 may notshare a user's personal information with skill developers, therebyrestricting skill developers' knowledge as to when is a most appropriatetime to output information to a user. An information selection component275 (of the natural language processing system 120) may have access touser personal information and other data points not available to a skilldeveloper, thereby making the information selection component 275 abetter determiner as to when information should, in fact, be output to auser, as well as what information to output.

The information selection component 275 may be configured to onlyprocess with respect to user identifiers and device identifiersassociated with data representing a user has provided permission for theinformation selection component 275 to output information to the user.Such data may be stored in a profiles corresponding to user identifierand device identifiers in the profile storage 270.

When the information selection component 275 is so configured, as aninitial step, the information selection component 275 may query theprofile storage 270 with respect to the user identifier and/or deviceidentifier represented in the output information request 505. If theuser identifier and/or device identifier is not associated with datarepresenting a user has provided permission for the informationselection component 275 to process, the query may return “no matchingsearch results” data representing the query was unable to identify data,associated with the user identifier and/or device identifier,representing a user has provide permission for the information selectioncomponent 275 to process. When the information selection component 275receives such data, the information selection component 275 maydetermine whether the present point in the skill session (represented inthe output information request 505) represents the end of the skillsession. If the information selection component 275 determines thepresent point in the skill session corresponds to the end of the skillsession, the information selection component 275 may simply ceaseprocessing (or output data indicating the skill session is to be ended).Conversely, if the information selection component 275 determines thepresent point in the skill session did not correspond to the end of theskill session (e.g., corresponded to an nth natural language input ofthe skill session, represented the skill session had transpired for namount of time, etc.), the information selection component 275 may send,to the skill 290, data representing the skill 290 is to recommenceprocessing with respect to the skill session. This data may effectivelypass the user experience back to the skill 290.

Alternatively, if the user identifier and/or device identifier is/areassociated with data representing a user has provided permission for theinformation selection component 275 to process, the query may return“confirmation” data representing the query was able to identify data,associated with the user identifier and/or device identifier,representing a user has provide permission for the information selectioncomponent 275 to process. When the information selection component 275receives such data, the information selection component 275 may querythe output information storage 255 for information associated with theskill identifier represented in the output information request 505. Eachpiece of information, in the output information storage 255, may beassociated with a respective information identifier. When the outputinformation storage 255 stores information identifiers, querying of theoutput information storage 255 may result in the information selectioncomponent 275 receiving one or more information identifiers 515associated with the skill identifier in the output information storage255.

Some information may correspond to something that may be purchased by auser (e.g., lives for a gaming skill, a music subscription, etc.). Itmay be undesirable to output information, pertaining to a purchasableitem, that the user has already purchased (e.g., an undesirable userexperience may result from outputting information specific to a musicsubscription when the user has already purchased the musicsubscription). To this end, after receiving the informationidentifier(s) 515, the information selection component 275 may query anentitlement component 510 to determine which of the informationidentifier(s) 515, corresponding to purchasable items, the user(corresponding to the user identifier) has already purchased.Specifically, the information selection component 275 may send, to theentitlement component 510, the information identifier(s) 515, and theuser identifier (represented in the output information request 505)and/or the device identifier (represented in the output informationrequest 505).

The entitlement component 510 may query a storage, including useridentifiers and/or device identifiers associated with informationidentifiers corresponding to purchased items, to determine if theinformation identifier(s) 515 is associated with the user identifier(represented in the output information request 505) and/or the deviceidentifier (represented in the output information request 505). If theentitlement component 510 determines none of the informationidentifier(s) 515 is/are associated with the user identifier or thedevice identifier, the entitlement component 510 may send, to theinformation selection component 275, “no matching search results” data.Conversely, if the entitlement component 510 determines one or more ofthe information identifier(s) 515 is associated with the user identifieror the device identifier in the storage, the entitlement component 510may send, to the information selection component 275, the determinedinformation identifier(s) 525.

The information selection component 275 may receive various context data535 for determining whether information is to be output to the user, aswell as determine which information (corresponding to informationidentifier(s) 515 minus the information identifier(s) 525) is to beoutput to the user. The information selection component 275 may beconfigured to prevent information from being output to the user if theinformation has been output to the user within a threshold amount oftime. To this end, the information selection component 275 may receiveand analyze context data 535 representing information identifierscorresponding to information that has been output to the user within thethreshold amount of time.

The information selection component 275 may additionally oralternatively be configured to prevent information from being output tothe user if the user has rejected the information within a thresholdamount of time. Such a user rejection may be embodied as negative userfeedback indicating the user was unhappy with the output of theinformation, may be embodied as a user declination to purchase an itemif the information queried the user to purchase the item, or may beembodied in some other form. To this end, the information selectioncomponent 275 may receive and analyze context data 535 representinginformation identifiers corresponding to information that has beenrejected by the user within the threshold amount of time.

The information selection component 275 may additionally oralternatively be configured to prevent information from being output tothe user if the user has rejected the information at least a thresholdnumber of times. To this end, the information selection component 275may receive and analyze context data 535 representing informationidentifiers corresponding to information that has been rejected by theuser at least a threshold number of times.

The information selection component 275 may be configured to outputinformation, associated with a skill, based on users acceptinginformation output with respect to other skills. A user's acceptance ofoutput information may be embodied as positive user feedback indicatingthe user was happy with the output of the information, may be embodiedas a user acceptance to purchase an item if the information queried theuser to purchase the item, may be embodied as the user requestingfurther information with respect to the output information, or may beembodied in some other form. The natural language processing system 120may determine, based on acceptances of information output with respectto various skills, a number of times a skill is to be invoked by a userprior to the natural language processing system 120 having the greatestconfidence that a user will accept output information. For example, thenatural language processing system 120 may determine a user is mostlikely to accept information, output with respect to a skill, after theuser has invoked the skill 2 times (e.g., caused the skill to performtwo different skill sessions with the user). Context data 535 mayrepresent an optimal number of skill invocations as determined by thenatural language processing system 120. The context data 535 may alsoinclude information representing a number of times the user(corresponding to the user identifier represented in the outputinformation request 505) has invoked the skill 290. The informationselection component 275 may analyze the two pieces of foregoing contextdata 535 to determine whether the user has invoked the skill 290 atleast the optimal number of times. This determination may be used toinform the information selection component 275's overall determinationas to whether information is to be output to the user.

Users in different geographic regions may have different propensities ofaccepting output information. The context data 535 may include ageographic region associated with the user identifier (represented inthe output information request 505) and/or the device identifier(represented in the output information request 505) in the profilestorage 270. The information selection component 275 may consider thegeographic region, represented in the context data 535, when determiningwhether to output information to the user.

Users of different ages may have different propensities of acceptingoutput information. The context data may include an age associated withthe user identifier (represented in the output information request 505)and/or the device identifier (represented in the output informationrequest 505) in the profile storage 270. The information selectioncomponent 275 may consider the age, represented in the context data 535,when determining whether to output information to the user.

A user's gender may be indicative of the user's propensity to acceptoutput information. The context data may include a gender associatedwith the user identifier (represented in the output information request505) and/or the device identifier (represented in the output informationrequest 505) in the profile storage 270. The information selectioncomponent 275 may consider the gender, represented in the context data535, when determining whether to output information to the user.

The information selection component 275 may additionally oralternatively receive and analyze context data 535 representing whethera profile (associated with the user identifier represented in the outputinformation request 505) includes data representing the user issubscribed to a pay-for service provided by the system 100, a price ofan item corresponding to information to be output, a type of theinformation to be output (e.g., whether the information corresponds to aone-time purchase of an item, whether the information corresponds thepurchase of a subscription, etc.), whether other users of the same skillhave accepted or rejected the output information, and/or other contextinformation that may be used in determining whether information is to beoutput to the user, as well as what information to output.

The information selection component 275 may implement one or moretrained machine learning models to determine whether information is tobe output to the user, as well as what information to output. In atleast some examples, the trained machine learning model(s) may be anon-deterministic model(s). The information (corresponding toinformation identifier(s) 515 minus the information identifier(s) 525)and the various context data 535 described herein may be input to themodel(s). The model(s) may process the various inputs and output anindicator represent whether information is to be output.

The machine learning model(s), implemented by the information selectioncomponent 275, may be trained and operated according to various machinelearning techniques. Such techniques may include, for example, neuralnetworks (such as deep neural networks and/or recurrent neuralnetworks), inference engines, trained classifiers, etc. Examples oftrained classifiers include Support Vector Machines (SVMs), neuralnetworks, decision trees, AdaBoost (short for “Adaptive Boosting”)combined with decision trees, and random forests. Focusing on SVM as anexample, SVM is a supervised learning model with associated learningalgorithms that analyze data and recognize patterns in the data, andwhich are commonly used for classification and regression analysis.Given a set of training examples, each marked as belonging to one of twocategories, an SVM training algorithm builds a model that assigns newexamples into one category or the other, making it a non-probabilisticbinary linear classifier. More complex SVM models may be built with thetraining set identifying more than two categories, with the SVMdetermining which category is most similar to input data. An SVM modelmay be mapped so that the examples of the separate categories aredivided by clear gaps. New examples are then mapped into that same spaceand predicted to belong to a category based on which side of the gapsthey fall on. Classifiers may issue a “score” indicating which categorythe data most closely matches. The score may provide an indication ofhow closely the data matches the category.

In order to apply machine learning techniques, the machine learningprocesses themselves need to be trained. Training a machine learningcomponent requires establishing a “ground truth” for the trainingexamples. In machine learning, the term “ground truth” refers to theaccuracy of a training set's classification for supervised learningtechniques. Various techniques may be used to train the models includingbackpropagation, statistical learning, supervised learning,semi-supervised learning, stochastic learning, or other knowntechniques.

The model(s), implemented by the information selection component 275,may apply weights to the different data points input to the model(s). Inat least some examples, the weights may be configured based on the skill290 that sent the output information request 505 to the informationselection component 275. For example, a first skill may be associatedwith a first set of weights, a second skill may be associated with asecond set of weights, etc. In at least some examples, weights,associated with a skill, may be provided by a skill developer of theskill through the skill developer's device. In at least some examples,machine learning may be used to determine weights to be applied by themodel(s). Such machine learning may consider previous instances wheninformation was output and a user rejected the information, previousinstances when information was output and a user accepted theinformation, and context data corresponding to each of the previousinstances.

If the indicator represents information is not to be output, theinformation selection component 275 may determine whether the presentpoint in the skill session (represented in the output informationrequest 505) represents the end of the skill session. If the informationselection component 275 determines the present point in the skillsession corresponds to the end of the skill session, the informationselection component 275 may simply cease processing (or output dataindicating the skill session is to be ended). Conversely, if theinformation selection component 275 determines the present point in theskill session did not correspond to the end of the skill session (e.g.,corresponded to an nth natural language input of the skill session,represented the skill session had transpired for n amount of time,etc.), the information selection component 275 may send, to the skill290, data representing the skill 290 is to recommence processing withrespect to the skill session. This data may effectively pass the userexperience back to the skill 290.

If the indicator represents information is to be output, the model(s)may also output an information identifier corresponding to theinformation to be output to the user. After the model(s) outputs theindicator (representing information is to be output) and the informationidentifier, the information selection component 275 may determine howthe information is to be output. To this end, the information selectioncomponent 275 may query (602 as illustrated in FIG. 6A) a prompt storage285 for prompt text associated with the information identifier(corresponding to the information to be output). Generally, the promptstorage 285 may store text corresponding to various prompts that may beoutput to users of the system 100. Prompt text, in the prompt storage285, may be associated with a skill identifier. Prompt text, in theprompt storage 285, may be associated with respective promptidentifiers.

In at least some examples, the prompt text may be provided to thenatural language processing system 120 by a skill developer through theskill developer's device. Moreover, in at least some examples, prompttext, in the prompt storage 285, may be associated with an informationidentifier and a skill identifier. In such examples, the informationselection component 275 may query the prompt storage 285 for prompt textassociated with the information identifier and the skill identifier(corresponding to the skill 290 that sent the output information request505) to the information selection component 275.

In response to the query, the information selection component 275 mayreceive (604) text corresponding a prompt associated with theinformation identifier (and optionally the skill identifier). Asdetailed above, prompt text may be provided by a skill developer. Inother examples, prompt text may be generated by the natural languageprocessing system 120. For example, a skill developer may simply providethe natural language processing system 120 with information to beoutput, and the natural language processing system 120 may generateprompt text for the information, with the resulting prompt text beingassociated with the information identifier (and optionally the skillidentifier) in the prompt storage 285. In at least some examples, skilldeveloper provided prompt text and natural language system generatedprompt text may be stored in different storages.

In at least some examples, the prompt text may include context inaddition to the information to be output. Such context may includephrases such as “this is something helpful for your skill,” “we have apersonalized offer just for you,” etc.

The information selection component 275 may determine (606) a manner inwhich the prompt text is to be output to the user. For example, theinformation selection component 275 may determine the prompt text is tobe output as audio (output from TTS processing of the prompt text) by adevice 110. For further example, the information selection component 275may determine the prompt text should be presented on a display of adevice 110. In another example, the information selection component 275may determine the prompt text is to be output as audio by a device 110and presented on a display of the same or a different device 110 (thedifferent devices may be associated with the same profile in the profilestorage 270). The information selection component 275's determination,as to how the prompt text is to be output, may be based on which mannersof outputting information to the user in the past resulted in acceptanceby the user. Such determination may additionally or alternatively bebased on output capabilities of the device 110 the user is using toconduct the skill session. For example, if the device 110 has speakersbut not display, the information selection component 275 may determinethe prompt text is to be output as audio using the speakers. For furtherexample, if the device 110 has speakers and a display, the informationselection component 275 may determine the prompt text is to be output asaudio using the speakers and/or presented on the display.

The information selection component 275 may send (608), to theorchestrator component 230, prompt configuration data. The promptconfiguration data 608 may include, for example, a device identifier(s)of the device(s) 110 to output the prompt, the user identifier of theuser, the skill identifier (corresponding to the skill 290 that sent theoutput information request 505 to the information selection component275), the skill session identifier, a present point of the skillsession, the information identifier (corresponding to the information tothe output), the prompt text, and data representing how the prompt textis to be output (e.g., as audio and/or text). In at least some examples,when the prompt configuration data indicates the prompt text is to beoutput as audio, the prompt configuration data may include speechsynthesis markup language (SSML), which indicates to the TTS component280 how synthesized speech is to be generated from the prompt text.

The orchestrator component 230 may generate (610) prompt data based onthe prompt configuration data. For example, if the prompt configurationdata indicates the prompt text is to be output as audio, theorchestrator component 230 may send the prompt text (and SSML if such isincluded in the prompt configuration data) to the TTS component 280, andmay in turn receive audio data corresponding to synthesized speechrepresenting the prompt text. For further example, if the promptconfiguration data indicates the prompt text is to be presented on adisplay, the prompt data may include the prompt text. In anotherexample, if the prompt configuration data indicates the prompt text isto be output as audio and presented on a display, the prompt data mayinclude audio data (output by the TTS component 280) and the prompttext. The prompt data may, in addition to including the audio dataand/or prompt text, include, for example, the user identifier of theuser, the skill identifier (corresponding to the skill 290 that sent theoutput information request 505 to the information selection component275), and/or the skill session identifier. In at least some examples,the prompt data may include an image or video to be presented on adisplay of a device 110 as part of output information to the user. Theorchestrator component 230 may send (610) the prompt data to one or moredevices 110 corresponding to the device identifier(s) represented in theprompt configuration data. For example, the prompt configuration datamay indicate a first device is to output audio and a second device is topresent text on its display. In such an example, the orchestratorcomponent 230 may send a portion of the prompt data (corresponding toprompt audio data) to the first device and a portion of the prompt data(corresponding to prompt text data) to the second device.

The device 110 may output (614) audio (corresponding to received promptaudio data) and/or present content (e.g., prompt text data, an image,and/or video) on a display. While outputting the audio and/or presentingthe content, or after outputting the audio and/or presenting thecontent, the device 110 may receive (616) a natural language input. Thenatural language input may correspond to natural language speech of theuser or a typed natural language input. The device 110 may send (618)natural language input data (e.g., audio data or text data depending onwhether the natural language input was spoken or typed, respectively) tothe orchestrator component 230.

The orchestrator component 230 may receive (620) NLU results datarepresenting the natural language input. If the natural language inputdata is audio data, the orchestrator component 230 may send the audiodata to the ASR component 250, and in turn receive text datarepresenting the natural language input. The orchestrator component 230may thereafter sent the text data to the NLU component 260, and in turnreceive NLU results data representing the natural language input.Alternatively, if the natural language input data is audio data, theorchestrator component 230 may send the audio data to an SLU component,and in turn receive NLU results data representing the natural languageinput. If the natural language input data is text data, the orchestratorcomponent 230 may send the text data to the NLU component 260, and inturn receive NLU results data representing the natural language input.

The orchestrator component 230 may determine (622) whether the NLUresults data indicates the natural language input accepts or rejects theinformation. For example, the orchestrator component 230 may determine a<Cancel> NLU intent, or other like NLU intent, may indicate the naturallanguage input rejects the information. For further example, theorchestrator component 230 may determine a <MoreInformation> NLU intent,<Purchase> NLU intent, or other like NLU intent indicates the naturallanguage input accepts the information.

If the orchestrator component 230 determines the NLU results dataindicates the natural language input rejects the information (e.g.,represents further information is not to be output, represents the userdoes not want to purchase a product corresponding to the outputinformation, etc.), the orchestrator component 230 may cease (624)processing or pass the user experience back to the skill. For example,the orchestrator component 230 may determine whether the present pointin the skill session (represented in the prompt configuration datareceived by the orchestrator component 230 at step 608) represents theend of the skill session. If the orchestrator component 230 determinesthe present point in the skill session corresponds to the end of theskill session, the orchestrator component 230 may simply ceaseprocessing (or output data indicating the skill session is to be ended).Conversely, if the orchestrator component 230 determines the presentpoint in the skill session did not correspond to the end of the skillsession (e.g., corresponded to an nth natural language input of theskill session, represented the skill session had transpired for n amountof time, etc.), the orchestrator component 230 may send, to the skill290 corresponding to the skill identifier represented in the promptconfiguration data received by the orchestrator component 230 at step608, data representing the skill 290 is to recommence processing withrespect to the skill session. This data may effectively pass the userexperience back to the skill 290.

If the orchestrator component 230 determines the NLU results dataindicates more information is to be output, the orchestrator component230 may communicate with the information selection component 275, theskill 290, and/or another component of the natural language processingsystem 120 to output more information regarding the information that wasoutput at step 614.

If the orchestrator component 230 determines the NLU results dataindicates the user does wants to purchase a product corresponding to theinformation output at step 614, the orchestrator component 230 may send(626), to a purchase skill 290 a, a purchase directive. The purchasedirective may include, for example, a device identifier of the device110 that received the natural language input at step 616 the useridentifier, the skill session identifier, the skill identifier(corresponding to the skill 290 that sent the output information request505 to the information selection component 275), a product identifier(corresponding to the information that was output to the user), and/or apurchase price.

The purchase skill 290 a may perform (628) a purchase flow. The purchaseflow may include the purchase skill 290 sending data to and receiveddata from the device 110 for purposes of performing a purchase withrespect to the product identifier and purchase price. For example, thepurchase flow may include the purchase skill 290 a, in conjunction withthe user recognition component 295, authenticating the user to ensurethe user has permission to perform the purchase. The purchase flow mayalso include the purchase skill 290 a sending, to a banking system(corresponding to a banking institution represented in a profileassociated with the user identifier in the profile storage 270) variousdetails of the purchase, and in turn receiving a confirmation that thepurchase has been approved by the banking system.

Once the purchase flow is complete, the purchase skill 290 a may cause arecord of the purchase to be stored. This stored record may bethereafter used by the model(s), implemented by the informationselection component 275, to ensure information, corresponding to thepurchased product, is not output to the user again (or to ensure theinformation is not output to the user too frequently).

Once the purchase flow is complete, the purchase skill 290 a may send(630), to the orchestrator component 230, purchase result data. Thepurchase result data may include, for example, the skill sessionidentifier, the skill identifier (corresponding to the skill 290 thatsent the output information request 505 to the information selectioncomponent 275), and a result of the purchase flow (e.g., success orfailed). If the purchase result data 630 represents the purchase wassuccessful, the orchestrator component 230 may send, to the skill 290corresponding to the skill identifier, data representing the skill 290is to recommence processing with respect to the skill session. This mayenable the user to begin using the purchased product.

Alternatively, after the orchestrator component 230 receives thepurchase result data, the orchestrator component 230 may cease (632)processing or pass the user experience back to the skill 290 based onwhether the present point in the skill session represents the end of theskill session. If the orchestrator component 230 determines the presentpoint in the skill session corresponds to the end of the skill session,the orchestrator component 230 may simply cease processing (or outputdata indicating the skill session is to be ended). Conversely, if theorchestrator component 230 determines the present point in the skillsession did not correspond to the end of the skill session (e.g.,corresponded to an nth natural language input of the skill session,represented the skill session had transpired for n amount of time,etc.), the orchestrator component 230 may send, to the skill 290, datarepresenting the skill 290 is to recommence processing with respect tothe skill session. This data may effectively pass the user experienceback to the skill 290.

The natural language processing system 120 may use user acceptance andrejection of output information to train the model(s) implemented by theinformation selection component 275. In at least some examples, useracceptances and rejections may be used to alter weights associated withinputs to the model(s).

In at least some examples, a user may perform a dialog with the naturallanguage processing system 120. As used herein, a “dialog” may refer totwo or more consecutive skill sessions that relate to a singleoriginating natural language input. For example, an initial naturallanguage input of the dialog may be “buy me a ticket to the movietonight at 7 pm.” This natural language input may correspond to a firstskill session corresponding to a first skill that communicates with anelectronic movie ticket system to buy the requested movie ticket. Oncethe movie ticket has been ordered, the natural language processingsystem 120 may receive a second natural language input corresponding tothe same dialog. For example, the second natural language input maycorrespond to “book me dinner reservation to my favorite restaurant at 5pm.” This second natural language input may correspond to a second skillsession corresponding to a second skill that communicates with arestaurant's online reservation portal to book the requestedreservation. Moreover, after the restaurant reservation has been booked,the natural language processing system 120 may receive a third naturallanguage input corresponding to the same dialog. For example, the thirdnatural language input may correspond to “book me a ride to my favoriterestaurant.” This third natural language input may correspond to a thirdskill session corresponding to a third skill that communicates with ataxi booking portal to book the requested ride.

In the foregoing illustrative dialog, each skill session may beassociated with a different skill session identifier but the same dialogidentifier. As such, a dialog identifier may be associated with multipleskill session identifiers. A dialog identifier may be used by componentsof the overall system 100 to track information across the dialog.

In some examples, a dialog-initiating natural language input may startwith a wakeword and end with a command. Subsequent natural languageinputs of the same dialog may not start with speaking of a wakeword.

In at least some examples, a first skill, corresponding to a skillsession, may indicate that information should be output for a secondskill corresponding to a subsequent skill session of the same dialog.For example, the first skill may be a restaurant reservation skill andthe second skill may be a taxi skill. The first skill may indicate thatthe user may save money on their restaurant reservation the user justbooked if the user invokes the taxi skill to book a ride to therestaurant. For further example, a second skill may indicate thatinformation for the second skill should be output at the end of a skillsession of a first skill. For example, a taxi skill may indicateinformation, representing a price discount for a taxi ride or otherwisesoliciting a user to purchase a taxi ride, should be output at the endof a skill session corresponding to a user interacting with restaurantreservation skill to book a reservation.

FIG. 7 is an illustrative diagram of a dialog system according toembodiments of the present disclosure. Components of the dialog systemmay reside as part of system 120 or may be otherwise configured. Thesystem receives input text data 702; the input text data 702 may includetext corresponding to a user input and metadata indicating furtherinformation about the text (such as an identity of the user, anemotional state of the user, etc.). The input text data 702 may be textrepresenting words, instructions, markup language, speech, or gestures,or may be a vector or other representation of the same. The input textdata 702 may be generated by a user via a keyboard, touchscreen,microphone, camera, or other such input device. In other examples, theinput text data 702 is created using ASR, as described above, from audiodata received from a user device. The system may further receive otherinput data 704, which may correspond to a button press, gesture, orother input. As described in greater detail below, using the input textdata 702 and/or other input data 704, the system may determine andoutput text data 706 and/or other output data 708. The system mayadditionally or alternatively perform an action based on the input textdata 702 and/or other input data 704, such as calling one or more APIs710.

An entity chunker 712 may be used to determine that the input text data702 includes a representation of one or more entities, a process thatmay include named entity recognition (NER) processing, which determinesthat the input text data 702 includes the representation, and entityresolution (ER) processing, which identifies a meaning or context of theentity, such as associating an identity of a person based on arecognized nickname. An entity may be a person, place, thing, idea,and/or goal. Example entities include proper names, nicknames, businessnames, place names, and/or application names.

In some examples, a single entity chunker 712 is used for more than onedomain (i.e., a “cross-domain” entity chunker 712). Each domain maycorrespond to one or more dialog models 714 (which are described ingreater detail below). In other embodiments, a plurality of entitychunkers 712 each correspond to a subset of the dialog models 714 (i.e.,“single-domain” entity chunkers 712). One or more candidate domainscorresponding to the input text data 702 may be determined by processingof the input text data 712 by entity chunkers 712 corresponding to thecandidate domains. Dialog focus data 716 may store the output entitiesfrom each candidate domain and may remove unselected entities whendialog model 714 is selected.

The dialog focus data 716 may store state data corresponding to dialoghistory data, action history data, and/or other data. In someembodiments, other components (e.g., an action selector 718) do notstore state data and instead query the dialog focus data 716 for thestate data. The system may send some or all of the dialog focus data 716to other systems and/or may receive additional dialog focus data fromother systems. In some embodiments, the other components (e.g., theaction selector 718) include a feature-extractor component to extractfeatures from the dialog focus data 716.

The dialog focus data 716 may be graph-based data including a pluralityof graph nodes. Each graph node may correspond to an item of state data,such as an entity type, entity value, prior API call, and/or user data.The other components, such as the action selector 718, may access all ofthe graph nodes of the dialog focus data 716 or may access only a subsetof the graph nodes of the dialog focus data 716. The dialog focus data716 may be any type of storage mechanism and may serve as long-termand/or short term memory for the system, thus enabling tracking ofentities, ASR output, TTS output, and other features through a dialog.In some examples, the dialog focus data 716 is updated after each turn(e.g., user input or system output) of dialog with updated dialog focusdata. In other embodiments, the dialog focus data 716 is updated afteran end of a dialog is determined.

The entity chunker 712 may utilize gazetteer information stored in anentity library storage. The gazetteer information may be used to matchtext data (representing a portion of the user input) with text datarepresenting known entities, such as song titles, contact names, etc.Gazetteers may be linked to users (e.g., a particular gazetteer may beassociated with a specific user's music collection), may be linked tocertain skills 290 (e.g., a shopping skill, a music skill, a videoskill, etc.), or may be organized in a variety of other ways.

For example, the entity chunker 712 may parse the input text data 702 toidentify words as subject, object, verb, preposition, etc. based ongrammar rules and/or models prior to recognizing named entities in thetext data. The entity chunker 712 may perform semantic tagging, which isthe labeling of a word or combination of words according to theirtype/semantic meaning. The entity chunker 712 may parse text data usingheuristic grammar rules, or a model may be constructed using techniquessuch as Hidden Markov Models, maximum entropy models, log linear models,conditional random fields (CRF), and the like. For example, an entitychunker 712 implemented by a music skill recognizer may parse and tagtext data corresponding to “play mother's little helper by the rollingstones” as {Verb}: “Play,” {Object}: “mother's little helper,” {ObjectPreposition}: “by,” and {Object Modifier}: “the rolling stones.” Theentity chunker 712 identifies “Play” as a verb based on a word databaseassociated with the music skill and may determine that the verbcorresponds to a <PlayMusic> intent.

The entity chunker 712 may tag text data to attribute meaning thereto.For example, the entity chunker 712 may tag “play mother's little helperby the rolling stones” as: {skill} Music, {intent}<PlayMusic>, {artistname} rolling stones, {media type} SONG, and {song title} mother'slittle helper. For further example, the entity chunker 312 may tag “playsongs by the rolling stones” as: {skill} Music, {intent}<PlayMusic>,{artist name} rolling stones, and {media type} SONG.

The entity chunker 712 may apply rules or other instructions totransform labels or tokens into a standard representation. Thetransformation may depend on the skill 290. For example, for a travelskill, the entity chunker 712 may transform text data corresponding to“Boston airport” to the standard BOS three-letter code referring to theairport. The entity chunker 712 can refer to an entity storage(s)(including text data representing entities known to the system) toresolve the precise entity referred to in each slot of each NLUhypothesis represented in the cross-skill N-best list data. Specificintent/slot combinations may also be tied to a particular source, whichmay then be used to resolve the text data. In the example “play songs bythe stones,” the entity chunker 712 may reference a personal musiccatalog, Amazon Music account, user profile data, or the like. Theentity chunker 712 may output text data including an altered N-best listthat is based on the cross-skill N-best list data, and that includesmore detailed information (e.g., entity IDs) about the specific entitiesmentioned in the slots and/or more detailed slot data that caneventually be used by a skill 290. The entity chunker 712 may includemultiple entity resolution components and each entity resolutioncomponent may be associated with one or more particular skills 290.

The entity chunker 712 may use frameworks linked to the intent todetermine what database fields should be searched to determine themeaning of tagged entities, such as searching a user's gazetteer forsimilarity with the framework slots. For example, a framework for a<PlayMusic> intent might indicate to attempt to resolve an identifiedobject based on {Artist Name}, {Album Name}, and {Song name}, andanother framework for the same intent might indicate to attempt toresolve an object modifier based on {Artist Name}, and resolve theobject based on {Album Name} and {Song Name} linked to an identified{Artist Name}. If the search of the gazetteer does not resolve aslot/field using gazetteer information, the entity chunker 712 maysearch a database of generic words associated with the skill 290. Forexample, if the text data includes “play songs by the rolling stones,”after failing to determine an album name or song name called “songs” by“the rolling stones,” the entity chunker 712 may search the skillvocabulary for the word “songs.” In the alternative, generic words maybe checked before the gazetteer information, or both may be tried,potentially producing two different results.

The entity chunker 712 may include a ranker component. The rankercomponent may assign a particular confidence score to each hypothesisinput therein. The confidence score of a hypothesis may represent aconfidence of the system in the processing performed with respect to thehypothesis. The confidence score of a particular hypothesis may beaffected by whether the hypothesis has unfilled slots. For example, if ahypothesis associated with a first skill component includes slots thatare all filled/resolved, that NLU hypothesis may be assigned a higherconfidence score than another hypothesis including at least some slotsthat are unfilled/unresolved by the entity chunker 712.

Dialog focus data 716 may store data relevant to a dialog. In variousembodiments, the dialog focus data 716 stores the input text data 702,other input data 704, entity data from the entity chunker 712 and/oraction data and dialog data from an action selector 718 (described ingreater detail below). The dialog focus data 716 may further includeadditional information, such as location data, user preference data, andenvironment data. In various embodiments, the dialog focus data 716 usesan encoder to encode some or all of the received data into one or morefeature vectors and a decoder to determine, based on the featurevectors, intent data corresponding to an intent of a user. The dialogfocus data 716 may further include state data that represents one ormore prior dialogs of the user, actions, or other prior user informationor data.

The dialog focus data 716 may be used by a dialog model predictor 720 toselect one or more dialog models 714 in dialog model storage 730 forfurther processing of the input text data 702. Each dialog model may beassociated with one or more categories of functions. The dialog modelpredictor 720 may be a trained model, such as a classifier. In variousembodiments, the dialog model predictor 720 generates an N-best list 722of the dialog models 714 for further processing. The dialog modelpredictor 720 may create the N-best list by determining a score for eachdialog model 714 given the dialog focus data 716 and model data 724. Themodel data 724 may include a type of each dialog model 714 and APIs andcorresponding entities for each dialog model 714. The dialog modelpredictor may, for example, determine the score based on presence orabsence of one or more entities determined by the entity chunker 712 inthe model data 724; presence of an entity in a list of entitiescorresponding to a dialog model 714 may, for example, indicate a higherscore. The dialog model predictor 720 may thus send the input text data702 to the models 714 having the N highest scores; in other embodiments,the dialog model predictor 720 sends the input text data 702 to themodels 714 having scores greater than a threshold. The threshold may bea numerical value or the number N of models 714 to be selected.

The selected dialog model(s) 714 may process the input text data 702; insome embodiments, the dialog model(s) 714 also receive the other inputdata 704 and/or dialog focus data 716. Each dialog model 714 may be atrained model, such as a sequence-to-sequence model, and may be trainedusing goal-oriented dialog training data. The training data may includea dialog between a user and a system and may include API callinformation related to goals expressed by the user.

Each of the selected dialog models 714 generates response data based onthe input text data 702. The response data may include output text data,which may correspond to a prompt for more information (e.g., additionalentity information). The response data may further include API call dataand corresponding entities.

The action selector 718 selects at least one of the outputs of thedialog model(s) 714 for further processing. Each output may beassociated with a corresponding category of function(s). The actionselector 718 may be a trained model, such as a classifier, and maydetermine a score for each of the outputs of the dialog models 714 basedon each's similarity or relevance to the dialog focus data 716, based onuser preference data, and/or based on the input text data 702. Theoutput of the dialog model 714 corresponding to the highest score isselected; if the output is an API call, one or more APIs 710 may beactivated and a corresponding action carried out. If, however, theoutput is a prompt or other output data, a natural language generator726 may be used to generate the output text data 706 and/or other outputdata 708 based on the output of the dialog model 714. In either case,the action may be used to update the dialog focus data 716.

In at least some examples, the information selection component 275 mayconsider the focus data 716 when determining whether information is tobe output to a user.

FIGS. 8A and 8B illustrate an example of how the information selectioncomponent 275 may execute at runtime. The information selectioncomponent 275 may receive (802) an output information request 505 from askill. The output information request 505 may include, for example, theskill's identifier, a domain to which the skill identifier isassociated, the skill session identifier, a user identifier(representing the user providing the natural language input(s) of theskill session), and/or a device identifier (representing the device 110that captured the natural language input(s) of the skill session).

After receiving the output information request 505, the informationselection component 275 may query (804) the output information storage255 for information associated with the skill identifier represented inthe output information request 505. Each piece of information, in theoutput information storage 255, may be associated with a respectiveinformation identifier. When the output information storage 255 storesinformation identifiers, querying of the output information storage 255may result in the information selection component 275 receiving one ormore information identifiers 515 associated with the skill identifier inthe output information storage 255.

The information selection component 275 may receive (806) first contextdata representing outputtable information that has been output to theuser within the threshold amount of time. The information selectioncomponent 275 may additionally or alternatively receive (808) secondcontext data representing outputtable information that has been rejectedby the user within the threshold amount of time. The informationselection component 275 may additionally or alternatively receive (810)third context data representing outputtable information that has beenrejected by the user at least a threshold number of times. The naturallanguage processing system 120 may receive (812) fourth context datarepresenting a number of times the user as acceptable outputinformation. The information selection component 275 may additionally oralternatively receive (814) fifth context data representing a geographicregion associated with the user. The information selection component 275may additionally or alternatively receive (816) sixth context datarepresenting an age of the user. The information selection component 275may additionally or alternatively receive (818) seventh context datarepresenting a gender of the user. The information selection component275 may additionally or alternatively receive (820) eighth context datarepresenting whether the user is subscribed to a pay-for service.

The information selection component 275 may implement one or moretrained machine learning models to determine (822) whether, based on thereceived context data, information is to be output to the user. If theinformation selection component 275 determines information is not to beoutput, the information selection component 275 may determine (824)whether the present point in the skill session is the end of the skillsession. If the information selection component 275 determines thepresent point in the skill session corresponds to the end of the skillsession, the information selection component 275 may send (826), to theorchestrator component 230, data indicating the skill session is to beended. Conversely, if the information selection component 275 determinesthe present point in the skill session did not correspond to the end ofthe skill session, the information selection component 275 may send(828), to the skill 290, data representing the skill 290 is torecommence processing with respect to the skill session.

If, at step 822, the information selection component 275 determines theoutput information is to be output in view of the context data, theinformation selection component 275 may cause steps 602-630 to beperformed. At the end of processing of step 630, the informationselection component 275 may then perform step 824 and 826 or 828.

FIG. 9 is a block diagram conceptually illustrating a device 110 thatmay be used with the system. FIG. 10 is a block diagram conceptuallyillustrating example components of a remote device, such as the naturallanguage processing system 120, which may assist with ASR processing,NLU processing, etc., and a skill system 225. A system (120/225) mayinclude one or more servers. A “server” as used herein may refer to atraditional server as understood in a server/client computing structurebut may also refer to a number of different computing components thatmay assist with the operations discussed herein. For example, a servermay include one or more physical computing components (such as a rackserver) that are connected to other devices/components either physicallyand/or over a network and is capable of performing computing operations.A server may also include one or more virtual machines that emulates acomputer system and is run on one or across multiple devices. A servermay also include other combinations of hardware, software, firmware, orthe like to perform operations discussed herein. The server(s) may beconfigured to operate using one or more of a client-server model, acomputer bureau model, grid computing techniques, fog computingtechniques, mainframe techniques, utility computing techniques, apeer-to-peer model, sandbox techniques, or other computing techniques.

Multiple systems (120/225) may be included in the overall system 100 ofthe present disclosure, such as one or more natural language processingsystems 120 for performing ASR processing, one or more natural languageprocessing systems 120 for performing NLU processing, one or more skillsystems 225, etc. In operation, each of these systems may includecomputer-readable and computer-executable instructions that reside onthe respective device (120/225), as will be discussed further below.

Each of these devices (110/120/225) may include one or morecontrollers/processors (904/1004), which may each include a centralprocessing unit (CPU) for processing data and computer-readableinstructions, and a memory (906/1006) for storing data and instructionsof the respective device. The memories (906/1006) may individuallyinclude volatile random access memory (RAM), non-volatile read onlymemory (ROM), non-volatile magnetoresistive memory (MRAM), and/or othertypes of memory. Each device (110/120/225) may also include a datastorage component (908/1008) for storing data andcontroller/processor-executable instructions. Each data storagecomponent (908/1008) may individually include one or more non-volatilestorage types such as magnetic storage, optical storage, solid-statestorage, etc. Each device (110/120/225) may also be connected toremovable or external non-volatile memory and/or storage (such as aremovable memory card, memory key drive, networked storage, etc.)through respective input/output device interfaces (902/1002).

Computer instructions for operating each device (110/120/225) and itsvarious components may be executed by the respective device'scontroller(s)/processor(s) (904/1004), using the memory (906/1006) astemporary “working” storage at runtime. A device's computer instructionsmay be stored in a non-transitory manner in non-volatile memory(906/1006), storage (908/1008), or an external device(s). Alternatively,some or all of the executable instructions may be embedded in hardwareor firmware on the respective device in addition to or instead ofsoftware.

Each device (110/120/225) includes input/output device interfaces(902/1002). A variety of components may be connected through theinput/output device interfaces (902/1002), as will be discussed furtherbelow. Additionally, each device (110/120/225) may include anaddress/data bus (924/1024) for conveying data among components of therespective device. Each component within a device (110/120/225) may alsobe directly connected to other components in addition to (or instead of)being connected to other components across the bus (924/1024).

Referring to FIG. 9, the device 110 may include input/output deviceinterfaces 902 that connect to a variety of components such as an audiooutput component such as a speaker 912, a wired headset or a wirelessheadset (not illustrated), or other component capable of outputtingaudio. The device 110 may also include an audio capture component. Theaudio capture component may be, for example, a microphone 920 or arrayof microphones, a wired headset or a wireless headset (not illustrated),etc. If an array of microphones is included, approximate distance to asound's point of origin may be determined by acoustic localization basedon time and amplitude differences between sounds captured by differentmicrophones of the array. The device 110 may additionally include adisplay 916 for displaying content. The device 110 may further include acamera 918.

Via antenna(s) 914, the input/output device interfaces 902 may connectto one or more networks 199 via a wireless local area network (WLAN)(such as WiFi) radio, Bluetooth, and/or wireless network radio, such asa radio capable of communication with a wireless communication networksuch as a Long Term Evolution (LTE) network, WiMAX network, 3G network,4G network, 5G network, etc. A wired connection such as Ethernet mayalso be supported. Through the network(s) 199, the system may bedistributed across a networked environment. The I/O device interface(902/1002) may also include communication components that allow data tobe exchanged between devices such as different physical servers in acollection of servers or other components.

The components of the device(s) 110, the natural language processingsystem 120, or a skill system 225 may include their own dedicatedprocessors, memory, and/or storage. Alternatively, one or more of thecomponents of the device(s) 110, the natural language processing system120, or a skill system 225 may utilize the I/O interfaces (902/1002),processor(s) (904/1004), memory (906/1006), and/or storage (908/1008) ofthe device(s) 110, natural language processing system 120, or the skillsystem 225, respectively. Thus, the ASR component 250 may have its ownI/O interface(s), processor(s), memory, and/or storage; the NLUcomponent 260 may have its own I/O interface(s), processor(s), memory,and/or storage; and so forth for the various components discussedherein.

As noted above, multiple devices may be employed in a single system. Insuch a multi-device system, each of the devices may include differentcomponents for performing different aspects of the system's processing.The multiple devices may include overlapping components. The componentsof the device 110, the natural language processing system 120, and askill system 225, as described herein, are illustrative, and may belocated as a stand-alone device or may be included, in whole or in part,as a component of a larger device or system.

As illustrated in FIG. 11, multiple devices (110 a-110 j, 120, 225) maycontain components of the system and the devices may be connected over anetwork(s) 199. The network(s) 199 may include a local or privatenetwork or may include a wide network such as the Internet. Devices maybe connected to the network(s) 199 through either wired or wirelessconnections. For example, a speech-detection device 110 a, a smart phone110 b, a smart watch 110 c, a tablet computer 110 d, a vehicle 110 e, adisplay device 110 f, a smart television 110 g, a washer/dryer 110 h, arefrigerator 110 i, and/or a microwave 110 j may be connected to thenetwork(s) 199 through a wireless service provider, over a WiFi orcellular network connection, or the like. Other devices are included asnetwork-connected support devices, such as the natural languageprocessing system 120, the skill system(s) 225, and/or others. Thesupport devices may connect to the network(s) 199 through a wiredconnection or wireless connection. Networked devices may capture audiousing one-or-more built-in or connected microphones or other audiocapture devices, with processing performed by ASR components, NLUcomponents, or other components of the same device or another deviceconnected via the network(s) 199, such as the ASR component 250, the NLUcomponent 260, etc. of the natural language processing system 120.

The concepts disclosed herein may be applied within a number ofdifferent devices and computer systems, including, for example,general-purpose computing systems, speech processing systems, anddistributed computing environments.

The above aspects of the present disclosure are meant to beillustrative. They were chosen to explain the principles and applicationof the disclosure and are not intended to be exhaustive or to limit thedisclosure. Many modifications and variations of the disclosed aspectsmay be apparent to those of skill in the art. Persons having ordinaryskill in the field of computers and speech processing should recognizethat components and process steps described herein may beinterchangeable with other components or steps, or combinations ofcomponents or steps, and still achieve the benefits and advantages ofthe present disclosure. Moreover, it should be apparent to one skilledin the art, that the disclosure may be practiced without some or all ofthe specific details and steps disclosed herein.

Aspects of the disclosed system may be implemented as a computer methodor as an article of manufacture such as a memory device ornon-transitory computer readable storage medium. The computer readablestorage medium may be readable by a computer and may compriseinstructions for causing a computer or other device to perform processesdescribed in the present disclosure. The computer readable storagemedium may be implemented by a volatile computer memory, non-volatilecomputer memory, hard drive, solid-state memory, flash drive, removabledisk, and/or other media. In addition, components of system may beimplemented as in firmware or hardware, such as an acoustic front end(AFE), which comprises, among other things, analog and/or digitalfilters (e.g., filters configured as firmware to a digital signalprocessor (DSP)).

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements, and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without other input or prompting,whether these features, elements, and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list.

Disjunctive language such as the phrase “at least one of X, Y, Z,”unless specifically stated otherwise, is understood with the context asused in general to present that an item, term, etc., may be either X, Y,or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, suchdisjunctive language is not generally intended to, and should not, implythat certain embodiments require at least one of X, at least one of Y,or at least one of Z to each be present.

As used in this disclosure, the term “a” or “one” may include one ormore items unless specifically stated otherwise. Further, the phrase“based on” is intended to mean “based at least in part on” unlessspecifically stated otherwise.

1.-20. (canceled)
 21. A computer-implemented method comprising:determining first data corresponding to timing for presentation ofinformation during an exchange with a natural language processingsystem; receiving, from a first device and after determining the firstdata, second data representing a first natural language input;determining a first application is to execute with respect to the firstnatural language input; sending, to the first application, third datarepresenting the first natural language input; processing at least thefirst data and dialog data using a first component to determine a firstindication that an ongoing first dialog corresponding to the firstnatural language input has reached a first point corresponding to thepresentation of information; determining first information to be outputat the first point; and causing, based at least in part on receiving thefirst indication, the first device to output the first information. 22.The computer-implemented method of claim 21, further comprising:determining context data corresponding to a user identifier associatedwith the ongoing first dialog, wherein determining the first data isbased at least in part on the context data.
 23. The computer-implementedmethod of claim 22, wherein the context data corresponds to a frequencya user has interacted with the first application.
 24. Thecomputer-implemented method of claim 22, wherein the context datacorresponds to a geographic location.
 25. The computer-implementedmethod of claim 21, further comprising: determining the first pointcorresponds to an end of the first dialog.
 26. The computer-implementedmethod of claim 21, wherein the first information corresponds to apurchase offer.
 27. The computer-implemented method of claim 21, whereinthe first information corresponds to a second application different fromthe first application.
 28. The computer-implemented method of claim 21,further comprising: after causing the first device to output the firstinformation, resuming the first dialog.
 29. The computer-implementedmethod of claim 21, wherein determining the first information comprisesusing a trained model to determine the first information.
 30. Thecomputer-implemented method of claim 21, wherein the first componentcomprises a trained model.
 31. A system comprising: at least oneprocessor; and at least one memory comprising instructions that, whenexecuted by the at least one processor, cause the system to: determinefirst data corresponding to timing for presentation of informationduring an exchange with a natural language processing system; receive,from a first device and after determining the first data, second datarepresenting a first natural language input; determine a firstapplication is to execute with respect to the first natural languageinput; send, to the first application, third data representing the firstnatural language input; process at least the first data and dialog datausing a first component to determine a first indication that an ongoingfirst dialog corresponding to the first natural language input hasreached a first point corresponding to the presentation of information;determine first information to be output at the first point; and cause,based at least in part on receiving the first indication, the firstdevice to output the first information.
 32. The system of claim 31,wherein the at least one memory further comprises instructions that,when executed by the at least one processor, further cause the systemto: determine context data corresponding to a user identifier associatedwith the ongoing first dialog, wherein determination of the first datais based at least in part on the context data.
 33. The system of claim32, wherein the context data corresponds to a frequency a user hasinteracted with the first application.
 34. The system of claim 32,wherein the context data corresponds to a geographic location.
 35. Thesystem of claim 31, wherein the at least one memory further comprisesinstructions that, when executed by the at least one processor, furthercause the system to: determine the first point corresponds to an end ofthe first dialog.
 36. The system of claim 31, wherein the firstinformation corresponds to a purchase offer.
 37. The system of claim 31,wherein the first information corresponds to a second applicationdifferent from the first application.
 38. The system of claim 31,wherein the at least one memory further comprises instructions that,when executed by the at least one processor, further cause the systemto: after causing the first device to output the first information,resume the first dialog.
 39. The system of claim 31, whereindetermination of the first information comprises using a trained modelto determine the first information.
 40. The system of claim 31, whereinthe first component comprises a trained model.